CT Pneumonia Detection
Accurate detection and differentiation of COVID-19 from community-acquired pneumonia and other lung diseases.
COVID-19 Research Results Published in Radiology
Overview
Aims to accurately detect COVID-19 and differentiate it from Community-Acquired Pneumonia (CAP) and other lung diseases.
Powered by cutting-edge artificial intelligence and medical image analysis technologies, DEEPSCAN helps radiologists diagnose and assess pneumonia progress efficiently.
Worklist – Triage and Notification
Real-time lung CT images analysis
Automated detection of COVID-19 and differentiation from other types of pneumonia
Radiologists can choose their review priorities based on notifications on the worklist
Improve the efficiency of detecting COVID-19 or other pneumonia suspected patients
Image Analysis
Overview
Detection Results
- Suspected COVID-19
- Suspected Pneumonia
- No inflammation identified
QA in Lung Lobes
- Schematic diagram of infected regions
- Quantitative assessment of lesion volume and lesion proportion
Lesion Histogram
- Hounsfield unit (HU) distribution within lesion regions
- Lesion volume and proportion grouped by different HU ranges
Findings
- Summary of findings in each lobe for direct use: lesion volume, proportion, and characteristics (GGO, consolidation)
Lesion Segmentation
Follow-Up Comparison
Validation
Clinical Datasets
Retrospective multi-center clinical validationHospitals
Patients
CT Scans
COVID-19 Scans
Workflow & Methods
COVID-19 detection neural network – COVNet: based on deep neural network, AI-powered
- Input: CT DICOM (Non-contrast CT, Slice thickness<=3mm)
- Output: Disease Classification Labels (COVID-19, CAP, Non-Pneumonia)
Detection Performance
COVID-19 Detection:
Sensitivity 90%; Specificity 96%; AUC 0.96
CAP Detection:
Sensitivity 87%; Specificity 92%; AUC 0.95
